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Transformers have become one of the foundational architectures in point cloud analysis tasks due to their excellent global modeling ability.
Über die stetige abbildung einer linie auf ein flächenstück
David Hilbert · 1935
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Shapenet: An information-rich 3d model repository
Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al · 2015
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3d shapenets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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A scalable active framework for region annotation in 3d shape collections
Li Yi, Vladimir G Kim, Duygu Ceylan, I-Chao Shen, Mengyan Yan, Hao Su, Cewu Lu, Qixing Huang, Alla Sheffer, and Leonidas Guibas · 2016
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Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
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A point set generation network for 3d object reconstruction from a single image
Haoqiang Fan, Hao Su, and Leonidas J Guibas · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
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Pointcnn: Convolution on x-transformed points
Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen · 2018
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Dgcnn: A convolutional neural network over large-scale labeled graphs
Anh Viet Phan, Minh Le Nguyen, Yen Lam Hoang Nguyen, and Lam Thu Bui · 2018
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Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data
Mikaela Angelina Uy, Quang-Hieu Pham, Binh-Son Hua, Thanh Nguyen, and Sai-Kit Yeung · 2019
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Dynamic graph cnn for learning on point clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Hippo: Recurrent memory with optimal polynomial projections
Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, and Christopher Ré · 2020
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Glu variants improve transformer
Noam Shazeer · 2020
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From points to parts: 3d object detection from point cloud with part-aware and part-aggregation network
Shaoshuai Shi, Zhe Wang, Jianping Shi, Xiaogang Wang, and Hongsheng Li · 2020
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Exploring self-attention for image recognition
Hengshuang Zhao, Jiaya Jia, and Vladlen Koltun · 2020
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Pra-net: Point relation-aware network for 3d point cloud analysis
Silin Cheng, Xiwu Chen, Xinwei He, Zhe Liu, and Xiang Bai · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
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Efficiently modeling long sequences with structured state spaces
Albert Gu, Karan Goel, and Christopher Re · 2021
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Combining recurrent, convolutional, and continuous-time models with linear state space layers
Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, and Christopher Ré · 2021
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Pct: Point cloud transformer
Meng-Hao Guo, Jun-Xiong Cai, Zheng-Ning Liu, Tai-Jiang Mu, Ralph R Martin, and Shi-Min Hu · 2021
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Mvtn: Multi-view transformation network for 3d shape recognition
Abdullah Hamdi, Silvio Giancola, and Bernard Ghanem · 2021
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Point transformer
Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip HS Torr, and Vladlen Koltun · 2021
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Crosspoint: Self-supervised cross-modal contrastive learning for 3d point cloud understanding
Mohamed Afham, Isuru Dissanayake, Dinithi Dissanayake, Amaya Dharmasiri, Kanchana Thilakarathna, and Ranga Rodrigo · 2022
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Autoencoders as cross-modal teachers: Can pretrained 2d image transformers help 3d representation learning?
Runpei Dong, Zekun Qi, Linfeng Zhang, Junbo Zhang, Jianjian Sun, Zheng Ge, Li Yi, and Kaisheng Ma · 2022
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Albert Gu, Karan Goel, Ankit Gupta, and Christopher Ré · 2022
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Albert Gu, Isys Johnson, Aman Timalsina, Atri Rudra, and Christopher Re · 2022
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Learning 3d representations from 2d pre-trained models via image-to-point masked autoencoders
Renrui Zhang, Liuhui Wang, Yu Qiao, Peng Gao, and Hongsheng Li · 2023
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Diagonal state spaces are as effective as structured state spaces
Ankit Gupta, Albert Gu, and Jonathan Berant · 2022
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Masked discrimination for self-supervised learning on point clouds
Haotian Liu, Mu Cai, and Yong Jae Lee · 2022
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Rethinking network design and local geometry in point cloud: A simple residual mlp framework
Xu Ma, Can Qin, Haoxuan You, Haoxi Ran, and Yun Fu · 2022
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Masked autoencoders for point cloud self-supervised learning
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Pointnext: Revisiting pointnet++ with improved training and scaling strategies
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